pers-bbi-conc-Gb245
## Loading tokens via the main script##
## Data:
## nsfg
## Network:
## p
## Terms:
## edges +
## nodecov("age") +
## nodecov("agesq") +
## nodefactor("race", base=5) +
## nodematch("race", diff=TRUE) +
## absdiff("sqrt.age.adj") +
## nodefactor("sex.ident", base=1:2) +
## degree(1, by="sb") +
## nodefactor("dsb.cohab", base=1)
## Offsets:
## offset(nodematch("sex", diff=FALSE))
## Constraints:
## ~bd(maxout=3)
## loadfit:
## FALSE
## Saving to:
## /net/proj/SHAMPnetdat/model-fits/archive/may-jkb## burnin=1e6, mcmc.int=1e51 Model information
Note: bb stands for “better burnin”
##
## --------------------------------------## [1] "Tuesday, September 04, 2018"## [1] "14:43"##
## --------------------------------------##
## Model: pers_bbi_conc_Gb245##
## nsfg data and network p## egoobj ~ edges + nodecov("age") + nodecov("agesq") + nodefactor("race",
## base = 5) + nodematch("race", diff = TRUE) + absdiff("sqrt.age.adj") +
## nodefactor("sex.ident", base = 1:2) + degree(1, by = "sb") +
## nodefactor("dsb.cohab", base = 1) + offset(nodematch("sex",
## diff = FALSE))##
## --------------------------------------2 Sufficient statistics
Edit this based on the model being assessed
summary_statistics_ego(as.formula(term.formula), popsize=50000)## Constructing pseudopopulation network.## Note: Constructed network has size 48210, different from requested 50000. Estimation should not be meaningfully affected.##
## Original weights summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0429 4.2370 11.5156 22.9393 27.3059 355.4855
##
## Unweighted weights summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1 1 1 1 1 1
##
## ----------------------------------------
## All stats scaled to popsize of 48210
## ----------------------------------------## Weighted Unweighted RatioWtoUW
## edges 4496.8 6795.0 0.7
## nodecov.age 256104.7 384226.7 0.7
## nodecov.agesq 7871208.2 11738983.7 0.7
## nodefactor.race.B 735.2 3907.9 0.2
## nodefactor.race.BI 354.2 581.1 0.6
## nodefactor.race.H 672.9 1670.9 0.4
## nodefactor.race.HI 483.4 988.5 0.5
## nodematch.race.B 257.6 1561.4 0.2
## nodematch.race.BI 97.6 89.9 1.1
## nodematch.race.H 99.8 346.6 0.3
## nodematch.race.HI 125.0 257.4 0.5
## nodematch.race.W 3040.5 2680.3 1.1
## absdiff.sqrt.age.adj 1407.7 2255.3 0.6
## nodefactor.sex.ident.msmf 64.8 85.1 0.8
## deg1.sbF.B 308.4 2247.2 0.1
## deg1.sbF.NB 3661.6 4551.1 0.8
## deg1.sbM.B 297.7 1317.5 0.2
## deg1.sbM.NB 4000.6 3953.9 1.0
## nodefactor.dsb.cohab.deg1pl.F.B 4.4 31.1 0.1
## nodefactor.dsb.cohab.deg1pl.F.NB 57.3 77.0 0.7
## nodefactor.dsb.cohab.deg1pl.M.B 10.5 40.5 0.3
## nodefactor.dsb.cohab.deg1pl.M.NB 107.5 109.5 1.0str(egoobj)## List of 4
## $ egos :'data.frame': 35677 obs. of 32 variables:
## ..$ weight : num [1:35677] 9.77 27.46 23.37 11.12 26.72 ...
## ..$ ego : int [1:35677] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ sex : chr [1:35677] "F" "F" "F" "F" ...
## ..$ age : num [1:35677] 41 37 42 38 43 44 42 44 39 40 ...
## ..$ sqrt.age : num [1:35677] 6.4 6.08 6.48 6.16 6.56 ...
## ..$ sex.ident : chr [1:35677] "f" "f" "f" "f" ...
## ..$ immigrant : chr [1:35677] "No" "No" "No" "No" ...
## ..$ role.class : chr [1:35677] "R" "R" "R" "R" ...
## ..$ otcount : num [1:35677] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ race : chr [1:35677] "W" "W" "W" "W" ...
## ..$ race3 : chr [1:35677] "W" "W" "W" "W" ...
## ..$ deg.cohab : num [1:35677] 0 1 0 1 1 0 1 0 1 1 ...
## ..$ deg.pers : num [1:35677] 1 0 1 0 0 1 0 1 0 0 ...
## ..$ raceimm : chr [1:35677] "W" "W" "W" "W" ...
## ..$ agesq : num [1:35677] 1681 1369 1764 1444 1849 ...
## ..$ demog.cat : num [1:35677] 1541 1537 1542 1538 1543 ...
## ..$ sqrt.age.adj : num [1:35677] 6.52 6.2 6.6 6.29 6.68 ...
## ..$ msmf : num [1:35677] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ agecat : chr [1:35677] "36-45" "36-45" "36-45" "36-45" ...
## ..$ race.sex : chr [1:35677] "W.F" "W.F" "W.F" "W.F" ...
## ..$ deg.pers.c : num [1:35677] 1 0 1 0 0 1 0 1 0 0 ...
## ..$ deg.cohab.c : num [1:35677] 0 1 0 1 1 0 1 0 1 1 ...
## ..$ dsb.pers : chr [1:35677] "deg1pl.F.NB" "deg0" "deg1pl.F.NB" "deg0" ...
## ..$ dsb.cohab : chr [1:35677] "deg0" "deg1pl.F.NB" "deg0" "deg1pl.F.NB" ...
## ..$ ds.pers : chr [1:35677] "deg1pl.F" "deg0" "deg1pl.F" "deg0" ...
## ..$ ds.cohab : chr [1:35677] "deg0" "deg1pl.F" "deg0" "deg1pl.F" ...
## ..$ sb : chr [1:35677] "F.NB" "F.NB" "F.NB" "F.NB" ...
## ..$ p.conc : chr [1:35677] "non-B.F" "non-B.F" "non-B.F" "non-B.F" ...
## ..$ xfour.conc : chr [1:35677] "non.B.BI.M.c-0" "non.B.BI.M.c-1" "non.B.BI.M.c-0" "non.B.BI.M.c-1" ...
## ..$ x.conc : chr [1:35677] "F.c-0" "non.B.BI.M.c-1.F.c-1" "F.c-0" "non.B.BI.M.c-1.F.c-1" ...
## ..$ race.sex.pers : chr [1:35677] "W.F.p1" "W.F.p0" "W.F.p1" "W.F.p0" ...
## ..$ race.sex.cohab: chr [1:35677] "W.F.p0" "W.F.p1" "W.F.p0" "W.F.p1" ...
## $ alters :'data.frame': 10057 obs. of 12 variables:
## ..$ ego : int [1:10057] 1 3 6 8 19 35 54 55 60 61 ...
## ..$ age : num [1:10057] 47 58 54 45 41 55 45 51 48 24 ...
## ..$ sqrt.age : num [1:10057] 6.86 7.62 7.35 6.71 6.4 ...
## ..$ sex : chr [1:10057] "M" "M" "M" "M" ...
## ..$ race : chr [1:10057] "W" "W" "W" "W" ...
## ..$ race3 : chr [1:10057] "W" "W" "W" "W" ...
## ..$ immigrant : chr [1:10057] "No" "No" "No" "No" ...
## ..$ len : num [1:10057] 36 54 43 68 1 67 26 18 156 7 ...
## ..$ raceimm : chr [1:10057] "W" "W" "W" "W" ...
## ..$ agesq : num [1:10057] 2209 3364 2916 2025 1681 ...
## ..$ sqrt.age.adj: num [1:10057] 6.86 7.62 7.35 6.71 6.4 ...
## ..$ race.sex : chr [1:10057] "W.M" "W.M" "W.M" "W.M" ...
## $ egoWt : num [1:35677] 9.77 27.46 23.37 11.12 26.72 ...
## $ egoIDcol: chr "ego"
## - attr(*, "class")= chr "egodata"3 Estimate or load models
startclock <- proc.time()Assign “fit” object, either from loaded models or by estimating a new model
if (loadfit) {
fit <- readRDS(rds.location)
} else {
fit <- ergm.ego(as.formula(model.call),
offset.coef = offset.coefs,
constraints=as.formula(constraints),
control=control.ergm.ego(ppopsize=50000,
ppop.wt='sample',
stats.est="asymptotic",
ergm.control =
control.ergm(MCMC.interval=1e5,
MCMC.samplesize=7500,
MCMC.burnin = 1e6,
MPLE.max.dyad.types = 1e7,
init.method = "zeros",
MCMLE.maxit = 400,
parallel = np,
parallel.type="PSOCK"
)))
cat('\n---------------------------------------\n')
cat('\nHOORAY, the model converged! Saving....\n')
saveRDS(fit, rds.location)
}## Constructing pseudopopulation network.## Unable to match target stats. Using MCMLE estimation.## Starting maximum likelihood estimation via MCMLE:## Iteration 1 of at most 400:## Optimizing with step length 0.020779006560131.## The log-likelihood improved by 4.468.## Iteration 2 of at most 400:## Optimizing with step length 0.0213045949830756.## The log-likelihood improved by 4.26.## Iteration 3 of at most 400:## Optimizing with step length 0.0228841719262391.## The log-likelihood improved by 4.485.## Iteration 4 of at most 400:## Optimizing with step length 0.0222005701526763.## The log-likelihood improved by 4.072.## Iteration 5 of at most 400:## Optimizing with step length 0.0229377524534442.## The log-likelihood improved by 4.219.## Iteration 6 of at most 400:## Optimizing with step length 0.0245385729709628.## The log-likelihood improved by 4.595.## Iteration 7 of at most 400:## Optimizing with step length 0.024641256790322.## The log-likelihood improved by 4.331.## Iteration 8 of at most 400:## Optimizing with step length 0.0254294595535608.## The log-likelihood improved by 4.529.## Iteration 9 of at most 400:## Optimizing with step length 0.0254818025695331.## The log-likelihood improved by 4.315.## Iteration 10 of at most 400:## Optimizing with step length 0.0247020625905488.## The log-likelihood improved by 3.921.## Iteration 11 of at most 400:## Optimizing with step length 0.0254334917978917.## The log-likelihood improved by 4.021.## Iteration 12 of at most 400:## Optimizing with step length 0.0246989427335922.## The log-likelihood improved by 3.786.## Iteration 13 of at most 400:## Optimizing with step length 0.0254332848859402.## The log-likelihood improved by 3.735.## Iteration 14 of at most 400:## Optimizing with step length 0.0278314632096548.## The log-likelihood improved by 4.35.## Iteration 15 of at most 400:## Optimizing with step length 0.0272178715030562.## The log-likelihood improved by 3.999.## Iteration 16 of at most 400:## Optimizing with step length 0.0271743389248697.## The log-likelihood improved by 4.028.## Iteration 17 of at most 400:## Optimizing with step length 0.0287442551286399.## The log-likelihood improved by 4.153.## Iteration 18 of at most 400:## Optimizing with step length 0.0272828248614569.## The log-likelihood improved by 3.81.## Iteration 19 of at most 400:## Optimizing with step length 0.0279656495432959.## The log-likelihood improved by 3.804.## Iteration 20 of at most 400:## Optimizing with step length 0.0280154340234283.## The log-likelihood improved by 3.679.## Iteration 21 of at most 400:## Optimizing with step length 0.0280190689730531.## The log-likelihood improved by 3.643.## Iteration 22 of at most 400:## Optimizing with step length 0.0288074663707811.## The log-likelihood improved by 3.853.## Iteration 23 of at most 400:## Optimizing with step length 0.0304459696757107.## The log-likelihood improved by 4.026.## Iteration 24 of at most 400:## Optimizing with step length 0.0305758834953522.## The log-likelihood improved by 4.095.## Iteration 25 of at most 400:## Optimizing with step length 0.032172375883868.## The log-likelihood improved by 4.3.## Iteration 26 of at most 400:## Optimizing with step length 0.033898311711593.## The log-likelihood improved by 4.705.## Iteration 27 of at most 400:## Optimizing with step length 0.0324513558604096.## The log-likelihood improved by 4.157.## Iteration 28 of at most 400:## Optimizing with step length 0.0323294171278058.## The log-likelihood improved by 4.187.## Iteration 29 of at most 400:## Optimizing with step length 0.0315227092640032.## The log-likelihood improved by 3.755.## Iteration 30 of at most 400:## Optimizing with step length 0.0330464252956803.## The log-likelihood improved by 4.049.## Iteration 31 of at most 400:## Optimizing with step length 0.0331773295387149.## The log-likelihood improved by 3.976.## Iteration 32 of at most 400:## Optimizing with step length 0.0347848171412601.## The log-likelihood improved by 4.487.## Iteration 33 of at most 400:## Optimizing with step length 0.0341288239466004.## The log-likelihood improved by 4.106.## Iteration 34 of at most 400:## Optimizing with step length 0.0356706012003544.## The log-likelihood improved by 4.27.## Iteration 35 of at most 400:## Optimizing with step length 0.0334045841848464.## The log-likelihood improved by 3.675.## Iteration 36 of at most 400:## Optimizing with step length 0.0348052913502519.## The log-likelihood improved by 3.938.## Iteration 37 of at most 400:## Optimizing with step length 0.0365343710277241.## The log-likelihood improved by 4.319.## Iteration 38 of at most 400:## Optimizing with step length 0.0350890874621015.## The log-likelihood improved by 3.889.## Iteration 39 of at most 400:## Optimizing with step length 0.0325522180711526.## The log-likelihood improved by 3.423.## Iteration 40 of at most 400:## Optimizing with step length 0.037119424032721.## The log-likelihood improved by 4.142.## Iteration 41 of at most 400:## Optimizing with step length 0.0399767860222244.## The log-likelihood improved by 4.51.## Iteration 42 of at most 400:## Optimizing with step length 0.0370277323540736.## The log-likelihood improved by 3.864.## Iteration 43 of at most 400:## Optimizing with step length 0.0383562531991609.## The log-likelihood improved by 4.107.## Iteration 44 of at most 400:## Optimizing with step length 0.0368720829575921.## The log-likelihood improved by 3.662.## Iteration 45 of at most 400:## Optimizing with step length 0.0399513299511839.## The log-likelihood improved by 4.225.## Iteration 46 of at most 400:## Optimizing with step length 0.0427034499930273.## The log-likelihood improved by 4.697.## Iteration 47 of at most 400:## Optimizing with step length 0.0421907872766029.## The log-likelihood improved by 4.423.## Iteration 48 of at most 400:## Optimizing with step length 0.0421349446738971.## The log-likelihood improved by 4.07.## Iteration 49 of at most 400:## Optimizing with step length 0.0421288684488979.## The log-likelihood improved by 4.229.## Iteration 50 of at most 400:## Optimizing with step length 0.0462050463054713.## The log-likelihood improved by 4.683.## Iteration 51 of at most 400:## Optimizing with step length 0.0425751077738953.## The log-likelihood improved by 3.735.## Iteration 52 of at most 400:## Optimizing with step length 0.0454416421232925.## The log-likelihood improved by 4.092.## Iteration 53 of at most 400:## Optimizing with step length 0.047421172621361.## The log-likelihood improved by 4.409.## Iteration 54 of at most 400:## Optimizing with step length 0.0460119657362954.## The log-likelihood improved by 4.002.## Iteration 55 of at most 400:## Optimizing with step length 0.0507820419485315.## The log-likelihood improved by 4.666.## Iteration 56 of at most 400:## Optimizing with step length 0.0472456212368906.## The log-likelihood improved by 3.743.## Iteration 57 of at most 400:## Optimizing with step length 0.0501169880613247.## The log-likelihood improved by 4.029.## Iteration 58 of at most 400:## Optimizing with step length 0.0496564796365911.## The log-likelihood improved by 3.81.## Iteration 59 of at most 400:## Optimizing with step length 0.0495977304853423.## The log-likelihood improved by 3.573.## Iteration 60 of at most 400:## Optimizing with step length 0.0529089807448927.## The log-likelihood improved by 3.803.## Iteration 61 of at most 400:## Optimizing with step length 0.0583751468447614.## The log-likelihood improved by 4.368.## Iteration 62 of at most 400:## Optimizing with step length 0.0583458087153091.## The log-likelihood improved by 4.221.## Iteration 63 of at most 400:## Optimizing with step length 0.0549559216455784.## The log-likelihood improved by 3.567.## Iteration 64 of at most 400:## Optimizing with step length 0.0595192170081394.## The log-likelihood improved by 3.717.## Iteration 65 of at most 400:## Optimizing with step length 0.0661545701222598.## The log-likelihood improved by 4.452.## Iteration 66 of at most 400:## Optimizing with step length 0.06899543580401.## The log-likelihood improved by 4.347.## Iteration 67 of at most 400:## Optimizing with step length 0.0668934756687018.## The log-likelihood improved by 3.844.## Iteration 68 of at most 400:## Optimizing with step length 0.0682618148327037.## The log-likelihood improved by 3.789.## Iteration 69 of at most 400:## Optimizing with step length 0.0754208521494146.## The log-likelihood improved by 4.059.## Iteration 70 of at most 400:## Optimizing with step length 0.0794238486481471.## The log-likelihood improved by 4.29.## Iteration 71 of at most 400:## Optimizing with step length 0.0766809988410379.## The log-likelihood improved by 3.554.## Iteration 72 of at most 400:## Optimizing with step length 0.0893695850688403.## The log-likelihood improved by 4.468.## Iteration 73 of at most 400:## Optimizing with step length 0.0913123476517793.## The log-likelihood improved by 4.109.## Iteration 74 of at most 400:## Optimizing with step length 0.0999303600045934.## The log-likelihood improved by 4.36.## Iteration 75 of at most 400:## Optimizing with step length 0.107617508608955.## The log-likelihood improved by 4.38.## Iteration 76 of at most 400:## Optimizing with step length 0.113414922835202.## The log-likelihood improved by 4.339.## Iteration 77 of at most 400:## Optimizing with step length 0.119774723704254.## The log-likelihood improved by 4.26.## Iteration 78 of at most 400:## Optimizing with step length 0.121639331726847.## The log-likelihood improved by 3.553.## Iteration 79 of at most 400:## Optimizing with step length 0.135681240302518.## The log-likelihood improved by 3.826.## Iteration 80 of at most 400:## Optimizing with step length 0.15215524970127.## The log-likelihood improved by 4.024.## Iteration 81 of at most 400:## Optimizing with step length 0.172635270505266.## The log-likelihood improved by 4.029.## Iteration 82 of at most 400:## Optimizing with step length 0.209365817227483.## The log-likelihood improved by 4.598.## Iteration 83 of at most 400:## Optimizing with step length 0.221303491466976.## The log-likelihood improved by 3.746.## Iteration 84 of at most 400:## Optimizing with step length 0.27297194270188.## The log-likelihood improved by 4.03.## Iteration 85 of at most 400:## Optimizing with step length 0.331827753531792.## The log-likelihood improved by 3.803.## Iteration 86 of at most 400:## Optimizing with step length 0.459145950450701.## The log-likelihood improved by 4.113.## Iteration 87 of at most 400:## Optimizing with step length 0.717482014956729.## The log-likelihood improved by 3.835.## Iteration 88 of at most 400:## Optimizing with step length 1.## The log-likelihood improved by 0.9899.## Step length converged once. Increasing MCMC sample size.## Iteration 89 of at most 400:## Optimizing with step length 1.## The log-likelihood improved by 0.04685.## Step length converged twice. Stopping.## Note: The constraint on the sample space is not dyad-independent. Null model likelihood is only implemented for dyad-independent constraints at this time. Number of observations is similarly ill-defined.## This model was fit using MCMC. To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.##
## ---------------------------------------
##
## HOORAY, the model converged! Saving....3.1 Fit or load time
Fit time: 802 minutes.
3.2 summary.ergm.ego
## Note: The constraint on the sample space is not dyad-independent. Null model likelihood is only implemented for dyad-independent constraints at this time. Number of observations is similarly ill-defined.##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: egoobj ~ edges + nodecov("age") + nodecov("agesq") + nodefactor("race",
## base = 5) + nodematch("race", diff = TRUE) + absdiff("sqrt.age.adj") +
## nodefactor("sex.ident", base = 1:2) + degree(1, by = "sb") +
## nodefactor("dsb.cohab", base = 1) + offset(nodematch("sex",
## diff = FALSE))
##
## Iterations: 89 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## netsize.adj -1.082e+01 0.000e+00 0 < 1e-04 ***
## edges 5.305e+00 9.549e-01 0 < 1e-04 ***
## nodecov.age -2.336e-01 3.375e-02 0 < 1e-04 ***
## nodecov.agesq 3.786e-03 5.585e-04 0 < 1e-04 ***
## nodefactor.race.B 1.295e+00 1.407e-01 0 < 1e-04 ***
## nodefactor.race.BI 1.830e+00 1.711e-01 0 < 1e-04 ***
## nodefactor.race.H 1.995e+00 1.055e-01 0 < 1e-04 ***
## nodefactor.race.HI 1.128e+00 1.145e-01 0 < 1e-04 ***
## nodematch.race.B 3.214e+00 1.346e-01 0 < 1e-04 ***
## nodematch.race.BI 2.815e+00 2.316e-01 0 < 1e-04 ***
## nodematch.race.H 2.633e-01 1.260e-01 0 0.036613 *
## nodematch.race.HI 2.263e+00 1.644e-01 0 < 1e-04 ***
## nodematch.race.W 2.162e+00 1.059e-01 0 < 1e-04 ***
## absdiff.sqrt.age.adj -2.594e+00 6.190e-02 0 < 1e-04 ***
## nodefactor.sex.ident.msmf -9.486e-01 2.819e-01 0 0.000764 ***
## deg1.sbF.B 1.046e+00 1.152e-01 0 < 1e-04 ***
## deg1.sbF.NB 1.164e+00 1.162e-01 0 < 1e-04 ***
## deg1.sbM.B 1.089e+00 1.634e-01 0 < 1e-04 ***
## deg1.sbM.NB 1.587e+00 2.808e-01 0 < 1e-04 ***
## nodefactor.dsb.cohab.deg1pl.F.B -4.415e+00 3.178e-01 0 < 1e-04 ***
## nodefactor.dsb.cohab.deg1pl.F.NB -5.349e+00 1.812e-01 0 < 1e-04 ***
## nodefactor.dsb.cohab.deg1pl.M.B -3.974e+00 2.862e-01 0 < 1e-04 ***
## nodefactor.dsb.cohab.deg1pl.M.NB -4.678e+00 1.733e-01 0 < 1e-04 ***
## nodematch.sex -Inf 0.000e+00 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## The following terms are fixed by offset and are not estimated:
## netsize.adj nodematch.sex4 mcmc.diagnostics()
mcmc.diagnostics(fit)## Warning in formals(fun): argument is not a function
5 model gof.ergm.ego
6 model gof.ergm.ego
GOF time: 41.5 minutes.
7 Observed vs fitted network
7.1 This model, burnin 1,000,000
obs_v_fitted_net(fit, attributes=c('sex', 'race'))##
## ----------------------------------------
## All stats refer to popsize of 50000
## ----------------------------------------
## PctDiff is %, e.g. 0.5 is 0.5% not 50%
## ----------------------------------------## $edges
## Term Target Fitted PctDiff
## 1 absdiff.sqrt.age.adj 1459.9 1460.5 0.0
## 2 deg1.sbF.B 319.9 321.0 0.3
## 3 deg1.sbF.NB 3797.6 3798.0 0.0
## 4 deg1.sbM.B 308.7 309.0 0.1
## 5 deg1.sbM.NB 4149.1 4149.0 0.0
## 6 edges 4663.8 4665.0 0.0
## 7 nodecov.age 265613.7 265705.0 0.0
## 8 nodecov.agesq 8163460.0 8166881.0 0.0
## 9 nodefactor.dsb.cohab.deg1pl.F.B 4.6 5.0 8.7
## 10 nodefactor.dsb.cohab.deg1pl.F.NB 59.4 60.0 1.0
## 11 nodefactor.dsb.cohab.deg1pl.M.B 10.9 11.0 0.9
## 12 nodefactor.dsb.cohab.deg1pl.M.NB 111.5 112.0 0.4
## 13 nodefactor.race.B 762.5 764.0 0.2
## 14 nodefactor.race.BI 367.4 367.0 -0.1
## 15 nodefactor.race.H 697.9 697.0 -0.1
## 16 nodefactor.race.HI 501.4 502.0 0.1
## 17 nodefactor.sex.ident.msmf 67.2 67.0 -0.3
## 18 nodematch.race.B 267.1 268.0 0.3
## 19 nodematch.race.BI 101.3 101.0 -0.3
## 20 nodematch.race.H 103.5 103.0 -0.5
## 21 nodematch.race.HI 129.7 130.0 0.2
## 22 nodematch.race.W 3153.4 3154.0 0.0
##
## $nodes
## $nodes$sex
## ObsPerc FittedPerc PctDiff
## F 49.5 49.1 -0.8
## M 50.5 50.9 0.8
##
## $nodes$race
## ObsPerc FittedPerc PctDiff
## B 3.8 3.8 0.0
## BI 2.5 2.5 0.0
## H 4.6 4.7 2.2
## HI 6.4 6.5 1.6
## W 82.8 82.6 -0.27.2 Prior model, burnin 7500, ppop.wt=‘round’
fitP <- readRDS('/net/proj/SHAMPnetdat/model-fits/archive/apr-jkb/pers_mix_E245.rds')
obs_v_fitted_net(fitP, attributes=c('sex', 'race'))##
## ----------------------------------------
## All stats refer to popsize of 48210
## ----------------------------------------
## PctDiff is %, e.g. 0.5 is 0.5% not 50%
## ----------------------------------------## $edges
## Term Target Fitted PctDiff
## 1 absdiff.sqrt.age.adj 1408.4 1390.1 -1.3
## 2 edges 4496.8 4434.0 -1.4
## 3 nodecov.age 256104.7 252853.0 -1.3
## 4 nodecov.agesq 7871208.2 7779711.0 -1.2
## 5 nodefactor.race.B 735.2 608.0 -17.3
## 6 nodefactor.race.BI 354.2 355.0 0.2
## 7 nodefactor.race.H 672.9 673.0 0.0
## 8 nodefactor.race.HI 483.4 483.0 -0.1
## 9 nodematch.race.B 257.6 194.0 -24.7
## 10 nodematch.race.BI 97.6 98.0 0.4
## 11 nodematch.race.H 99.8 100.0 0.2
## 12 nodematch.race.HI 125.0 125.0 0.0
## 13 nodematch.race.W 3040.5 3041.0 0.0
##
## $nodes
## $nodes$sex
## ObsPerc FittedPerc PctDiff
## F 49.5 49 -1
## M 50.5 51 1
##
## $nodes$race
## ObsPerc FittedPerc PctDiff
## B 3.8 2.3 -39.5
## BI 2.5 2.6 4.0
## H 4.6 3.8 -17.4
## HI 6.4 6.1 -4.7
## W 82.8 85.2 2.98 Total runtime
Approximately 844 minutes.